Executive insights: How advanced AI is fueling lending transformation

AI is advancing every day, with newer, better models always around the corner. Navigating this quickly changing world of technology on their own can be challenging, complex and costly for financial service providers. But there is a better way.

Lenders are already seeing the benefits of AI in lending and by partnering with innovative technology providers that offer vertical-specific financial AI solutions, they can take advantage of the latest advancements without constantly reworking their own technology stack or processes. Technology partners handle updates and integrations, continuously tuning models with high-quality training data to enhance accuracy, improve decision-making and provide deeper insights as technology continues to evolve.

We sat down with Ocrolus CEO Sam Bobley, President Vik Dua and SVP of Growth David Snitkof to discuss how this will help drive innovation in mortgage, small business and consumer lending.

As AI capabilities evolve, lenders who rely on manual processes or outdated tech risk slower approvals and missed opportunities. Partnering with a vertical-specific AI provider like Ocrolus ensures they stay current with the latest innovations without overhauling their own systems.

Sam: Improving AI means more efficient lending processes

Accuracy is paramount when it comes to financial decision-making. Foundational AI models are advancing rapidly, but they remain imperfect and are not yet capable of fully autonomous processing. Each model—whether from OpenAI, Google or Amazon—employs a distinct methodology for expressing confidence levels, making it challenging to assess their effectiveness and determine the best fit for a given task.

At Ocrolus, we solve this challenge with universal calibration, a proprietary technique that standardizes model outputs into a consistent, measurable framework. This enables Ocrolus to dynamically select the most effective AI model for each task, ensuring optimal performance. As foundational models evolve, universal calibration allows both Ocrolus and our customers to benefit from enhanced functionality and cost efficiencies.

By integrating multiple industry-leading open- and closed-source models—enhanced with proprietary training data and supported by our human-in-the-loop verification process—Ocrolus consistently delivers 99%+ accuracy, while continuously improving speed and reducing costs.

Vik: Adding value in the application layer

Foundational AI models play a crucial role in processing vast amounts of data, but the real value of AI emerges at the application layer. This is where purpose-built tools translate data into relevant and actionable insights.

Every vertical has its own unique needs and challenges that they are looking to AI to address. For lenders, AI might streamline complex income calculations in mortgage underwriting or provide better visibility into a borrower’s financial health through cash flow analysis for small business funding. Ocrolus delivers this value by bridging the gap between raw data and intelligent decision-making. Our platform doesn’t just extract and structure financial data—it applies advanced analytics tailored specifically for lending workflows.

Forward-thinking lenders are already integrating these specialized applications within their workflows, enjoying gains in efficiency, scalability and improved decision-making that come with purpose-built financial AI. They benefit from automated yet auditable decision support, empowering underwriters with the confidence to approve loans faster. Those who fail to adopt a similar approach risk falling behind in an industry where speed, accuracy and data-driven insights increasingly define success.

David: Continual improvement through trusted training data

AI models are improving at an impressive rate. As models gain ever-better reasoning capability and more broadly applicable intelligence, they can be used to solve a wider set of problems. In order to utilize these models – whether commercially available, open-source or trained in-house – it’s critical to tune the technology to the task at hand. As mentioned above, universal calibration allows us to choose the appropriate model for each task and evaluate its performance consistently. The same vast quantity of training data that powers our calibration capability also enables us to train in-house models for specific tasks where it would be advantageous. This ongoing learning is what makes high-quality data the most valuable resource for AI applications. Without reliable, labeled training data, machine learning and AI models will produce inaccurate predictions and exhibit poor performance.

At Ocrolus, we have a distinct advantage with our completely in-house data labeling capability. Rather than relying on external sources, we create our own high-quality training data as a byproduct of processing millions of applications for mortgage, small business and consumer loan applications each year.

This unparalleled library of financial data, combined with the latest AI integrations, empowers us to continually deliver advanced automation and analytical capabilities and insights that drive more informed lending decisions for our customers.

To learn how Ocrolus’ purpose-built financial document automation and analysis solutions can help you make the most of advances in AI, book a demo today.

Key takeaways

• Advances in foundational AI technology provide lenders with more opportunities to evaluate and integrate new models, introduce new capabilities and improve efficiency.
• Purpose-built financial AI applications help lenders automate manual tasks, from complex income calculations to getting a better view of borrowers’ financial health with cash flow analysis.
• High-quality, labeled AI training data is the most valuable resource for AI, ensuring accurate predictions and informed lending decisions.